Fuzzy neural network modelling for hydrological studies

Simple item page
dc.contributor.authorDeka, Paresh Chandra
dc.date.accessioned2020-08-13T10:22:12Z
dc.date.accessioned2023-10-19T12:35:15Z
dc.date.available2020-08-13T10:22:12Z
dc.date.available2023-10-19T12:35:15Z
dc.date.issued2003
dc.descriptionSupervisors: V Chandramouli and Anjan Duttaen_US
dc.description.abstractWater resources related studies involve variables, which are highly random and uncertain in nature. Most hydrological variables exhibit a high degree of temporal and spatial variability. These studies are very essential to the mankind for providing a warning of the extreme flood or drought conditions and help to optimize the operation of systems like reservoirs and power plants etc. For better hydrological design, we need proper modelling of the system using these variables. Many approaches were suggested in the past. In this research study, a new modelling approach that uses artificial neural network and fuzzy logic concepts together is proposed for modelling hydrological problems.en_US
dc.identifier.otherROLL NO. 994702
dc.identifier.urihttps://gyan.iitg.ac.in/handle/123456789/1579
dc.language.isoenen_US
dc.relation.ispartofseriesTH-1856;
dc.subjectCIVIL ENGINEERINGen_US
dc.titleFuzzy neural network modelling for hydrological studiesen_US
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
Abstrcat-TH-1856_994702.pdf
Size:
363.28 KB
Format:
Adobe Portable Document Format
Description:
Abstract
Download
No Thumbnail Available
Name:
TH-1856_994702.pdf
Size:
48.68 MB
Format:
Adobe Portable Document Format
Description:
Thesis
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Plain Text
Description:
Download
Collections
PhD Theses (Civil Engineering)